Scaling the Price Calculation Engine for hospital patients having varying insurance coverage and from multiple providers.

Case Study

Distributed Computing

Scaling the Price Calculation Engine for hospital patients having varying insurance coverage and from multiple providers.

Sector: HeathCare Industry | Year: 2021 | Country: USA

Objectives

Legacy component, too core and risky to redevelop for scaling. Extract out & Go Distributed.

  • I. Quicker accounting for hospital patients.
  • II. Scale up the pricing engine (complex & bottleneck).
  • III. Minimal testing requirements, or change to core logic.

Key challenges

The main tasks that we faced were:

  • 01Deemed too risky to redevelop.
  • 02Part of a legacy system that needed to be modernized.
  • 03No SME, critical, and dependent on 3rd party libs!.

Approach

The idea of portal is to consolidate data generated from multiple sources, enhance performance across all quality measures, and improve population health.

  • 01/
    Extract out the component.
  • 02/
    Parallel billing for multiple patients using core engine.
  • 03/
    Scale using distributed containers, coordinated on MQ.

Technology

It became really important for us to focus on building an IT Infrastructure that can support the rapid development of new solutions to meet both the organizational goals and customer demands.

Featured List

Comprehensive overview of MRA, HEDIS, & more

Interactive, user-friendly dashboard

HIPAA Compliance & Data Security

Web, iOS, and Android compatibility

Extensive reports feature, with export capability

Performance & productivity tracking

Role-based user profiles, including Member access

Member <-> Provider communication

  • Scaled to 10 times faster solution & more if required.
  • Reduced single points of failure.

    Results

  • Zero complaints.